R Notebook

Load the lib (you might want to install it first).

library(bibliometrix)   ### load bibliometrix package
library(htmlwidgets) # to have saveWidget
library(data.table) # for the sources tables

Import the wos and scopus publication files.

file_wos <- "wos_2018_2023.bib"
file_scopus <- "scopus_2018_2024.bib"
M_wos <- convert2df(file = file_wos, dbsource = "wos", format = "bibtex")

Converting your wos collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!

M_scopus <- convert2df(file = file_scopus, dbsource = "scopus", format = "bibtex")

Converting your scopus collection into a bibliographic dataframe


Warning:
In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!

Please, take a look at the vignettes:
- 'Data Importing and Converting' (https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html)
- 'A brief introduction to bibliometrix' (https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html)


Missing fields:  CR 
Done!


Generating affiliation field tag AU_UN from C1:  Done!


Removed  9 duplicated documents

First bibliometric analysis:

results_wos <- biblioAnalysis(M_wos, sep = ";")
results_scopus <- biblioAnalysis(M_scopus, sep = ";")
write.csv(results_wos$Sources, "sources_wos.csv", row.names=FALSE)
write.csv(results_scopus$Sources, "sources_scopus.csv", row.names=FALSE)

Basic plots for WoS data.

plot(x = results_wos, k = 10, pause = FALSE)

Avis : Removed 1 rows containing non-finite values (`stat_align()`).

Basic plots for Scopus data.

plot(x = results_scopus, k = 10, pause = FALSE)

Avis : Removed 1 rows containing non-finite values (`stat_align()`).

Some citation analysis with WoS data.

CR <- citations(M_wos, field = "article", sep = ";")
cbind(CR$Cited[1:10])

                                                                                                                                     [,1]
LOWE DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94                                                   14
RONNEBERGER O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4\\_28                                             11
ANONYMOUS, P IEEE C COMP VIS PA                                                                                                        10
INGLADA J, 2017, REMOTE SENS-BASEL, V9, DOI 10.3390/RS9010095                                                                          10
GOODCHILD MF, 2007, GEOJOURNAL, V69, P211, DOI 10.1007/S10708-007-9111-Y                                                                8
MACKANESS W., 2002, P JOINT ISPRSICA WOR, P7                                                                                            8
TOUYA G, 2010, T GIS, V14, P595, DOI 10.1111/J.1467-9671.2010.01215.X                                                                   8
TOUYA G, 2019, INT J CARTOGRAPHY, V5, P142, DOI 10.1080/23729333.2019.1613071                                                           8
COUCLELIS H., 1987, J ENVIRON PSYCHOL, V7, P99, DOI DOI 10.1016/S0272-4944(87)80020-8, HTTPS://DOI.ORG/10.1016/S0272-4944(87)80020-8    7
GIRRES JF, 2010, T GIS, V14, P435, DOI 10.1111/J.1467-9671.2010.01203.X                                                                 7

A <- cocMatrix(M_wos, Field = "SO", sep = ";")
sort(Matrix::colSums(A), decreasing = TRUE)[1:10]

                                      ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION                                                                       REMOTE SENSING 
                                                                                  12                                                                                   12 
                                                INTERNATIONAL JOURNAL OF CARTOGRAPHY                         30TH INTERNATIONAL CARTOGRAPHIC CONFERENCE (ICC 2021), VOL 4 
                                                                                   9                                                                                    7 
       IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM                                   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 
                                                                                   7                                                                                    7 
                                                       REMOTE SENSING OF ENVIRONMENT                                        2019 JOINT URBAN REMOTE SENSING EVENT (JURSE) 
                                                                                   5                                                                                    4 
2020 MEDITERRANEAN AND MIDDLE-EAST GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (M2GARSS)                            INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 
                                                                                   4                                                                                    4 

A <- cocMatrix(M_wos, Field = "CR", sep = ".  ")
sort(Matrix::colSums(A), decreasing = TRUE)[1:10]

                                                                                ACHANTA R 2012 IEEE T PATTERN ANAL  
                                                                                                                  2 
                                                                                       ADAMO M 2014 LANDSCAPE ECOL  
                                                                                                                  2 
                                                                              ADHIKARI B 2018 LECT NOTES ARTIF INT  
                                                                                                                  2 
                                                                                ADLER BT 2011 LECT NOTES COMPUT SC  
                                                                                                                  2 
                                                                                           ALI I 2016 J PLANT ECOL  
                                                                                                                  2 
                                                                              ALTAMIMI Z 2016 J GEOPHYS RES-SOL EA  
                                                                                                                  2 
ALVINA J 2014 PROCEEDINGS OF THE 2014 INTERNATIONAL WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES AVI 2014 P125  
                                                                                                                  2 
                                                                                AMAZIRH A 2018 REMOTE SENS ENVIRON  
                                                                                                                  2 
                                                           ANONYMOUS 2006 INT ARCH PHOTOGRAM 3 BOUDET L 2006 IAPRS  
                                                                                                                  2 
                                                                         ANOOSHEH A 2019 IEEE INT CONF ROBOT P5958  
                                                                                                                  2 

The main sources for publication according to WoS data:

data.table(results_wos$Sources)

The main sources for publication according to Scopus data:

data.table(results_scopus$Sources)

Create keyword co-occurrences network with WoS data:

NetMatrix <- biblioNetwork(M_wos, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 25, Title = "WoS Keyword Co-occurrences", type = "fruchterman", size=T, edgesize = 4,labelsize=1.0,community.repulsion = 0.1,size.cex = T,halo = F)

Create keyword co-occurrences network with Scopus data:

NetMatrix <- biblioNetwork(M_scopus, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 20, Title = "Scopus Keyword Co-occurrences", type = "fruchterman", size=T, edgesize = 4,labelsize=1.0,community.repulsion = 0.1,size.cex = T,halo = F)

Create classical article coupling network with WoS data:

NetMatrix <- biblioNetwork(M_wos, analysis = "coupling", network = "references", sep = ";")
# Plot the network
net=networkPlot(NetMatrix,  normalize = "salton", weighted=NULL, n = 30, Title = "WoS Article' Coupling", type = "fruchterman", size=10,size.cex=T,remove.multiple=TRUE,labelsize=1.0,label.n=20,label.cex=F)

Create authors’ coupling network with WoS data:

NetMatrix <- biblioNetwork(M_wos, analysis = "coupling", network = "authors", sep = ";")
net=networkPlot(NetMatrix,  normalize = "salton", weighted=T, n = 20, Title = "WoS Authors' Coupling", type = "fruchterman", size = 5,size.cex=T,remove.multiple=TRUE,labelsize=2.0,label.n=20,label.cex = T)

Conceptual Structure using keywords (method=“CA”) with WoS data:

options(ggrepel.max.overlaps = Inf)
CS <- conceptualStructure(M_wos,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)

Conceptual Structure using keywords (method=“CA”) with Scopus data:

CS <- conceptualStructure(M_scopus,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)

Create a historical citation network with WoS data:

options(width=200)
histResults <- histNetwork(M_wos, min.citations = 1, sep = ";")

WOS DB:
Searching local citations (LCS) by reference items (SR) and DOIs...

Analyzing 9716 reference items...

Found 22 documents with no empty Local Citations (LCS)

# Plot a historical co-citation network
net <- histPlot(histResults, n=20, size = 10, labelsize=5)

 Legend

Read the synonym and ignored keywords files.

#synonyms_file <- read.csv("synonyms.csv")
synonyms_file <- scan("synonyms.csv",sep='\n', what = "", quiet = TRUE)
synonyms_file <- gsub(",", "; ", synonyms_file)
synonyms_file <- c(synonyms_file)
remove_file <- scan("remove.csv",sep='\n', what = "", quiet = TRUE)
remove_file <- remove_file

Now, create the thematic map using the synonyms with WoS data:

res <- thematicMap(M_wos, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE, synonyms = synonyms_file, remove.terms = remove_file)
plot(res$map)

Now, create the thematic map using the synonyms and the author’s keywords with WoS data:

res <- thematicMap(M_wos, field = "DE", n = 250, minfreq = 5, size = 0.5, repel = TRUE, synonyms = synonyms_file, remove.terms = remove_file)
plot(res$map)

The same but with the Scopus data:

res <- thematicMap(M_scopus, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE, synonyms = synonyms_file, remove.terms = remove_file)
plot(res$map)

The same but with the Scopus data and the author’s keywords:

res <- thematicMap(M_scopus, field = "DE", n = 250, minfreq = 5, size = 0.5, repel = TRUE, synonyms = synonyms_file, remove.terms = remove_file)
plot(res$map)

We add the function to create wordlists.

wordlist <- function(M, Field, n, measure, ngrams, remove.terms=NULL, synonyms=NULL){
  switch(Field,
         ID={v=tableTag(M,"ID", remove.terms  = remove.terms, synonyms = synonyms)},
         DE={v=tableTag(M,"DE", remove.terms = remove.terms, synonyms = synonyms)},
         TI={
           if (!("TI_TM" %in% names(M))){
             v=tableTag(M,"TI", ngrams=ngrams, remove.terms=remove.terms, synonyms = synonyms)
             
           }},
         AB={if (!("AB_TM" %in% names(M))){
           v=tableTag(M,"AB", ngrams=ngrams, remove.terms = remove.terms, synonyms = synonyms)
         }},
         WC={
           v=tableTag(M,"WC")
         }
  )
  names(v)=tolower(names(v))
  #v=tableTag(values$M,"ID")
  n=min(c(n,length(v)))
  Words=data.frame(Terms=names(v)[1:n], Frequency=(as.numeric(v)[1:n]), stringsAsFactors = FALSE)
  W=Words
  switch(measure,
         identity={},
         sqrt={W$Frequency=sqrt(W$Frequency)},
         log={W$Frequency=log(W$Frequency+1)},
         log10={W$Frequency=log10(W$Frequency+1)}
  )
  
  results=list(v=v,W=W, Words=Words)
  return(results)
}

resW <- wordlist(M=M_wos, Field="ID", n=150, measure="identity", ngrams=1, synonyms = synonyms_file, remove.terms = remove_file)
W <- resW$W
write.csv(resW[["W"]], "data_wos.csv", row.names=FALSE)
Words <- resW$Words
w1 <- wordcloud2::wordcloud2(W, size = 1.5, minRotation = 0, maxRotation = 0, rotateRatio = 1)#minRotation = -pi/2, maxRotation = -pi/2)
saveWidget(w1, 'w1.html', selfcontained = T)

Wordcloud with WoS and keywords plus.

resW <- wordlist(M=M_wos, Field="DE", n=150, measure="identity", ngrams=1, remove.terms = remove_file, synonyms = synonyms_file)
W <- resW$W
write.csv(resW[["W"]], "data_wos_de.csv", row.names=FALSE)
Words <- resW$Words
w2 <- wordcloud2::wordcloud2(W, size = 1.5, minRotation = 0, maxRotation = 0, rotateRatio = 1)#minRotation = -pi/2, maxRotation = -pi/2)
saveWidget(w2, 'w2.html', selfcontained = T)

Wordcloud with WoS and author’s keywords.

resW <- wordlist(M=M_scopus, Field="ID", n=150, measure="identity", ngrams=1, remove.terms = remove_file, synonyms = synonyms_file)
W <- resW$W
write.csv(resW[["W"]], "data_scopus.csv", row.names=FALSE)
Words <- resW$Words
w3 <- wordcloud2::wordcloud2(W, size = 1.5, color = 'random-dark', minRotation = 0, maxRotation = 0, rotateRatio = 1)
saveWidget(w3, 'w3.html', selfcontained = T)

Wordcloud with Scopus and keywords plus.

resW <- wordlist(M=M_scopus, Field="DE", n=150, measure="identity", ngrams=1, remove.terms = remove_file, synonyms = synonyms_file)
W <- resW$W
write.csv(resW[["W"]], "data_scopus_de.csv", row.names=FALSE)
Words <- resW$Words
w4 <- wordcloud2::wordcloud2(W, size = 1.5, color = 'random-dark', minRotation = 0, maxRotation = 0, rotateRatio = 1)
saveWidget(w4, 'w4.html', selfcontained = T)

Wordcloud with Scopus and author’s keywords.

We are done!